"Fragments of cloth, bits of cotton, lumps of earth:" Lists in American Literature, 1851-1956
Bibliographic record
Abstract
This project traces literary catalogues through roughly a century of American literature and takes seriously the list as a distinct and rhetorically significant aesthetic category. While inventories and enumerations are typically meant to regulate and organize information, when housed within literature they behave quite differently. Literary lists can be overwhelming and boring; they can arrest narrative progression and ask us to pause for a moment of meticulous accounting. In the first chapter, I argue that Herman Melville’s Moby-Dick; or, The Whale leaves the reader awash in a world teeming with whale facts and figures. These “oceanic” lists, I assert, toggle somewhere between the metaphorical and the literal, playing at a scientific certainty that is ultimately left unfulfilled. Chapter Two reimagines Walt Whitman’s catalogues in “Song of Myself” as initiating a rhetorical oscillation between the singular and plural, a process that works to repopulate the cultural representation of America with subjects who have traditionally been excluded or marginalized. In Chapter Three I position Gertrude Stein’s Tender Buttons as a collection of everyday objects left suspended somewhere between utility and aesthetics, and in Chapter Four I re-envision William Faulkner as an author-carpenter who builds a six-sided narrative “coffin” with his rotating list of characters in As I Lay Dying. In Chapter Five, I argue that James Agee reaches for an ethical relation with human beings through obsessive catalogues of their material things in Let Us Now Praise Famous Men, and my concluding chapter argues that Allen Ginsberg’s “Howl” enacts what I call an “aesthetic unrest” through its insistent crowding and overwhelming amount of activity. Contrary to what some scholars identify as the list’s potentially dangerous cultural function, I argue that literary lists perform a kind of strange oscillation: a restless tension that sits unresolved and ultimately registers an ethical engagement with otherness itself. Because these lists are so fundamentally strange, I conclude, they encourage us to reconsider not only that which is familiar and comforting, but also the ideological grounds upon which those distinctions rest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".